The DARE Project: Dynamic Abstraction-driven Replanning and Execution

It is often beneficial for an autonomous agent that operates in a complex
environment to make use of different types of mathematical models to keep track of
unobservable parts of the world or to perform prediction, planning and other types
of reasoning. There always exists a tradeoff between the model's accuracy and
feasibility when it is used within a certain application, due to the limited
available computational resources. In most cases, this tradeoff is to a large
extent balanced by humans for model construction in general and for autonomous
agents in particular.

The DARE project has investigated different solutions where agents are more
responsible for balancing the tradeoff for models themselves, in the context of
interleaved task planning and plan execution. The necessary components for an
autonomous agent that performs its abstractions and constructs planning models
dynamically during task planning and execution have been investigated. The DARE
method is a template for handling the possible situations that can occur such as the
rise of unsuitable abstractions and need for dynamic construction of abstraction
levels. Implementations of DARE have been presented in two case studies where both
a fully and partially observable stochastic domain are used, motivated by research
with Unmanned Aircraft Systems. The case studies also demonstrate possible ways to
perform dynamic abstraction and problem model construction in practice.